In order to train all networks, the convolutional layers were kept and only the classifiers were changed to fit the task requirements. In addition, all models were pretrained on ImageNet [1]}. All models were fine-tuned for 150 epochs using the Adam optimization [2]} with a starting learning rate equal to \(0.0001\)  and the batch size equal to 32. The learning rate is scheduled to be reduced by a factor of 0.2 if the models fail to improve the validation loss for 10 epochs. Finally, we use early stopping, also based on a stagnant validation loss for 15 epochs.
